The Role for Imaging in the Investigation of Isolated Objective Vestibular Weakness
Bibliographic record
Abstract
OBJECTIVE: Unilateral vestibular weakness has considerable potential etiologies. One source is a vestibular schwannoma. This article evaluates, in the absence of other symptoms and signs, if unilateral vestibular weakness is an analogue to asymmetric sensorineural hearing loss and serves as an indication for lateral skull base imaging. STUDY DESIGN: Retrospective chart review. SETTING: Academic tertiary center. SUBJECTS AND METHODS: All patients undergoing caloric assessment between January 1, 2012, and June 30, 2018, were investigated. Patients with unilateral vestibular weakness (a left-right difference >25% on electronystagmography) were included in the study. A provincial encompassing image library was surveyed for potential adequate imaging (computed tomography internal auditory canal infused, magnetic resonance imaging [MRI] brain, MRI internal auditory canal) of the target population within the preceding 5 years. Presence/absence of vestibular schwannoma on imaging was determined. RESULTS: Of the 3531 electronystagmography reports reviewed during the period, 864 patients were identified with unilateral vestibular weakness. Of these, 542 had sufficient imaging, and 14 vestibular schwannomas were identified. Only 1 individual had a vestibular weakness in isolation, while the remaining 13 patients also suffered from documented sensorineural hearing loss that would have mandated MRI scanning. CONCLUSION: The results of our study suggest that, in isolation, vestibular weakness is an insufficient indicator for lateral skull base imaging.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".